Deep metric learning: A survey
M Kaya, HŞ Bilge - Symmetry, 2019 - mdpi.com
Metric learning aims to measure the similarity among samples while using an optimal
distance metric for learning tasks. Metric learning methods, which generally use a linear …
distance metric for learning tasks. Metric learning methods, which generally use a linear …
TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining
G Rice, T Wagner, M Stabrin, O Sitsel, D Prumbaum… - Nature …, 2023 - nature.com
Cryogenic-electron tomography enables the visualization of cellular environments in
extreme detail, however, tools to analyze the full amount of information contained within …
extreme detail, however, tools to analyze the full amount of information contained within …
Deep learning for free-hand sketch: A survey
Free-hand sketches are highly illustrative, and have been widely used by humans to depict
objects or stories from ancient times to the present. The recent prevalence of touchscreen …
objects or stories from ancient times to the present. The recent prevalence of touchscreen …
Deep learning methods of cross-modal tasks for conceptual design of product shapes: A review
Conceptual design is the foundational stage of a design process that translates ill-defined
design problems into low-fidelity design concepts and prototypes through design search …
design problems into low-fidelity design concepts and prototypes through design search …
Toward fine-grained sketch-based 3D shape retrieval
In this paper we study, for the first time, the problem of fine-grained sketch-based 3D shape
retrieval. We advocate the use of sketches as a fine-grained input modality to retrieve 3D …
retrieval. We advocate the use of sketches as a fine-grained input modality to retrieve 3D …
A novel center-boundary metric loss to learn discriminative features for hyperspectral image classification
S Mei, Z Han, M Ma, F Xu, X Li - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Learning discriminative features is crucial for hyperspectral image (HSI) classification.
Though metric learning has been applied to learn effective features in HSI classification …
Though metric learning has been applied to learn effective features in HSI classification …
D2GL: Dual-level dual-scale graph learning for sketch-based 3D shape retrieval
W Li, J Bai, H Zheng - Pattern Recognition, 2024 - Elsevier
Sketch-based 3D shape retrieval (SBSR) is an active research area in the computer vision
community, but it is still very challenging. One main reason is that existing deep learning …
community, but it is still very challenging. One main reason is that existing deep learning …
Domain disentangled generative adversarial network for zero-shot sketch-based 3d shape retrieval
Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy
between sketches and 3D shapes. Since existing methods are trained and evaluated on the …
between sketches and 3D shapes. Since existing methods are trained and evaluated on the …
Hda2l: Hierarchical domain-augmented adaptive learning for sketch-based 3d shape retrieval
S Bai, J Bai - Knowledge-Based Systems, 2023 - Elsevier
The sketch-based 3D shape retrieval has been an active but challenging task for several
decades. In this paper, we deeply analyze the challenges and propose a novel Hierarchical …
decades. In this paper, we deeply analyze the challenges and propose a novel Hierarchical …
Uncertainty learning for noise resistant sketch-based 3d shape retrieval
Recently, sketch-based 3D shape retrieval has received growing attention in the community
of computer graphics and computer vision. Most previous works focus on the problem of how …
of computer graphics and computer vision. Most previous works focus on the problem of how …